Sequential Change Detection

Sequential Change Detection is a statistical methodology for on-line change-point detection that sequentially analyzes data streams in real time to distinguish the presence of a structural break or distributional shift from stationary behavior by applying recursive decision rules to incoming observations.

209

Publications

10.3K

Citations

381

Authors

211

Institutions

Publications per year

2017–2026

44

Authors

381

  1. IV
    Igor V. Nikiforov

    Université de Technologie de Troyes, France

    8 publications · 381 citations

    8

    h-index

  2. AG
    Alexander G. Tartakovsky

    University of Southern California, United States

    8 publications · 561 citations

    8

    h-index

  3. GV
    George V. Moustakides

    University of Patras, Greece

    8 publications · 310 citations

    8

    h-index

  4. MP
    Moshe Pollak

    Hebrew University of Jerusalem, Israel

    7 publications · 281 citations

    7

    h-index

  5. YX
    Yao Xie

    Georgia Institute of Technology, United States

    7 publications · 260 citations

    7

    h-index

1–5 of 381

Venues

  1. The Annals of Statistics

    13 publications · 1.1K citations

    13

    h-index

  2. IEEE Transactions on Information Theory

    13 publications · 557 citations

    13

    h-index

  3. Sequential Analysis

    12 publications · 492 citations

    12

    h-index

  4. IEEE Transactions on Signal Processing

    6 publications · 155 citations

    6

    h-index

  5. arXiv (Cornell University)

    Repository · 5 publications · 111 citations

    5

    h-index

1–5